Name
Abstract | ||
|---|---|---|
Maritime transports play a critical role in international trade and commerce. Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial-temporal patterns of vessel navigations. Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management. As essential techniques in complex data analysis and understanding, visualization and visual analysis have been widely used in vessel trajectory data analysis. This paper presents a literature review on the visualization and visual analysis of vessel trajectory data. First, we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data preprocessing. Then, we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details. Finally, we expound on the prospects of the remaining challenges and directions for future research. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.visinf.2021.10.002 | VISUAL INFORMATICS |
Keywords | DocType | Volume |
Maritime traffic, Vessel trajectory data, Automatic identification system, Visualization and visual analysis | Journal | 5 |
Issue | ISSN | Citations |
4 | 2468-502X | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyan Liu | 1 | 0 | 1.01 |
| Xiaohui Chen | 2 | 0 | 1.69 |
| Yidi Wang | 3 | 0 | 0.34 |
| Bing Zhang | 4 | 0 | 0.34 |
| Yunpeng Chen | 5 | 0 | 0.34 |
| Ying Zhao | 6 | 219 | 21.13 |
| Fangfang Zhou | 7 | 42 | 3.67 |